[TaskFlow] Fix pir for taskflow

llm/predict/predictor.py

@@ -24,7 +24,7 @@
 import numpy as np
 import paddle
 import paddle.incubate.multiprocessing as mp
-from paddle.base.framework import in_cinn_mode, in_pir_executor_mode, use_pir_api
+from paddle.base.framework import in_cinn_mode, in_pir_executor_mode
 from paddle.distributed import fleet
 
 try:
@@ -51,7 +51,12 @@
     PretrainedTokenizer,
 )
 from paddlenlp.trl import llm_utils
-from paddlenlp.utils.env import MAX_BSZ, MAX_DRAFT_TOKENS
+from paddlenlp.utils.env import (
+    MAX_BSZ,
+    MAX_DRAFT_TOKENS,
+    PADDLE_INFERENCE_MODEL_SUFFIX,
+    PADDLE_INFERENCE_WEIGHTS_SUFFIX,
+)
 from paddlenlp.utils.import_utils import is_paddlenlp_ops_available
 from paddlenlp.utils.log import logger
 
@@ -670,10 +675,11 @@ def _create_predictor(self, predictor_args: PredictorArgument):
         infer_model_path = llm_utils.get_infer_model_path(
             predictor_args.model_name_or_path, predictor_args.model_prefix
         )
-        if use_pir_api():
-            config = paddle.inference.Config(infer_model_path + ".json", infer_model_path + ".pdiparams")
-        else:
-            config = paddle.inference.Config(infer_model_path + ".pdmodel", infer_model_path + ".pdiparams")
+
+        config = paddle.inference.Config(
+            infer_model_path + PADDLE_INFERENCE_MODEL_SUFFIX,
+            infer_model_path + PADDLE_INFERENCE_WEIGHTS_SUFFIX,
+        )
 
         config.switch_ir_optim(True)
         # remove `gpu_cpu_map_matmul_v2_to_matmul_pass` to avoid mapping matmul_v2 -> matmul op
@@ -1103,7 +1109,7 @@ def predict(self, input_texts: list[str], return_tokens=False):
                 self.full_hidden_states = self._infer(self.model_inputs)
             else:
                 self._infer(self.model_inputs)
-        logger.info(f"running spend {time.time()  -  s_time}")
+        logger.info(f"running spend {time.time() - s_time}")
 
         if self.proposer is not None:
             self.proposer.postprocess(base_model_inputs=self.model_inputs)
@@ -1190,10 +1196,10 @@ def _create_predictor(self, predictor_args: PredictorArgument):
             predictor_args.model_name_or_path, predictor_args.model_prefix
         )
 
-        if use_pir_api():
-            config = paddle.inference.Config(infer_model_path + ".json", infer_model_path + ".pdiparams")
-        else:
-            config = paddle.inference.Config(infer_model_path + ".pdmodel", infer_model_path + ".pdiparams")
+        config = paddle.inference.Config(
+            infer_model_path + PADDLE_INFERENCE_MODEL_SUFFIX,
+            infer_model_path + PADDLE_INFERENCE_WEIGHTS_SUFFIX,
+        )
 
         config.switch_ir_optim(False)
         if predictor_args.device in paddle.device.get_all_custom_device_type():
@@ -1230,7 +1236,7 @@ def predict(self, input_texts: list[str], return_tokens=False):
             self.proposer.insert_query(
                 base_model_inputs=self.model_inputs, real_bs=len(input_texts), seq_lens=self.seq_lens
             )
-        logger.info(f"preprocess spend {time.time()  -  s_time}")
+        logger.info(f"preprocess spend {time.time() - s_time}")
 
         result_queue = mp.Queue()
         tensor_queue = mp.Queue()
@@ -1269,7 +1275,7 @@ def predict(self, input_texts: list[str], return_tokens=False):
                 self.full_hidden_states = self.predictor.run(list(self.model_inputs.values()))[0]
             else:
                 self.predictor.run(list(self.model_inputs.values()))
-        logger.info(f"running spend {time.time()  -  s_time}")
+        logger.info(f"running spend {time.time() - s_time}")
 
         if self.proposer is not None:
             self.proposer.postprocess(base_model_inputs=self.model_inputs)
@@ -1303,7 +1309,7 @@ def create_predictor(
         config: PretrainedConfig,
         model_args: ModelArgument,
         tokenizer: PretrainedTokenizer = None,
-        **kwargs
+        **kwargs,
     ):
         """
         Create a predictor

llm/server/docs/deploy_usage_tutorial.md

@@ -99,7 +99,7 @@ cd /home/workspace/models_dir
 # ├── rank_mapping.csv           # 多卡模型会有此文件，如为单卡模型，则无此文件（可选，仅在多卡部署模式下需要）
 # └── rank_0                     # 保存模型结构和权重文件的目录
 #     ├── model.pdiparams
-#     └── model.pdmodel
+#     └── model.pdmodel 或者 model.json # Paddle 3.0 版本模型为model.json，Paddle 2.x 版本模型为model.pdmodel
 ```
 
 ### 创建容器

paddlenlp/experimental/autonlp/text_classification.py

@@ -47,6 +47,7 @@
 from ...utils.log import logger
 from .auto_trainer_base import AutoTrainerBase
 from .utils import UTCLoss
+from .utils.env import PADDLE_INFERENCE_MODEL_SUFFIX, PADDLE_INFERENCE_WEIGHTS_SUFFIX
 
 
 class AutoTrainerForTextClassification(AutoTrainerBase):
@@ -560,16 +561,16 @@
         if os.path.exists(default_export_path):
             if "utc" in model_config["model_name_or_path"]:
                 files = [
-                    "model.pdiparams",
-                    "model.pdmodel",
+                    f"model{PADDLE_INFERENCE_WEIGHTS_SUFFIX}",
+                    f"model{PADDLE_INFERENCE_MODEL_SUFFIX}",
                     "tokenizer_config.json",
                     "vocab.txt",
                     "taskflow_config.json",
                 ]
             else:
                 files = [
-                    "model.pdiparams",
-                    "model.pdmodel",
+                    f"model{PADDLE_INFERENCE_WEIGHTS_SUFFIX}",
+                    f"model{PADDLE_INFERENCE_MODEL_SUFFIX}",
                     "tokenizer_config.json",
                     "vocab.txt",
                     "taskflow_config.json",
@@ -735,8 +736,8 @@
             executor=exe,
             batch_generator=_batch_generator_func,
             model_dir=export_path,
-            model_filename="model.pdmodel",
-            params_filename="model.pdiparams",
+            model_filename=f"model{PADDLE_INFERENCE_MODEL_SUFFIX}",
+            params_filename=f"model{PADDLE_INFERENCE_WEIGHTS_SUFFIX}",
             batch_size=batch_size,
             batch_nums=batch_nums,
             scope=None,
@@ -757,8 +758,8 @@
         post_training_quantization.quantize()
         post_training_quantization.save_quantized_model(
             save_model_path=compress_path,
-            model_filename="model.pdmodel",
-            params_filename="model.pdiparams",
+            model_filename=f"model{PADDLE_INFERENCE_MODEL_SUFFIX}",
+            params_filename=f"model{PADDLE_INFERENCE_WEIGHTS_SUFFIX}",
         )
 
         paddle.disable_static()

paddlenlp/generation/utils.py

@@ -1437,55 +1437,28 @@
             outputs, input_ids, cur_len_gpu, origin_len_gpu, scores, unfinished_flag, model_kwargs, pad_token_id
         )
 
-        if hasattr(paddle.framework, "_no_check_dy2st_diff"):
-            # TODO(daisiming): _no_check_dy2st_diff is used to turn off the checking of behavior
-            # inconsistency between dynamic graph and static graph. _no_check_dy2st_diff should be
-            # removed after static graphs support inplace and stride.
-            with paddle.framework._no_check_dy2st_diff():
-                paddle.increment(cur_len)
-                paddle.increment(cur_len_gpu)
-        else:
-            paddle.increment(cur_len)
-            paddle.increment(cur_len_gpu)
+        cur_len += 1
+        cur_len_gpu += 1
 
         attn_mask = model_kwargs["attention_mask"]
         # make the shape of attention_mask = (-1, -1, -1, -1) in dy2static.
         model_kwargs["attention_mask"] = paddle.reshape(attn_mask, attn_mask.shape)
         model_kwargs["cache"] = outputs[1] if isinstance(outputs, tuple) else None
         max_new_tokens = paddle.full([1], max_new_tokens + cur_len - 1, dtype="int64")
 
-        if hasattr(paddle.framework, "_no_check_dy2st_diff"):
-            # TODO(daisiming): _no_check_dy2st_diff is used to turn off the checking of behavior
-            # inconsistency between dynamic graph and static graph. _no_check_dy2st_diff should be
-            # removed after static graphs support inplace and stride.
-            with paddle.framework._no_check_dy2st_diff():
-                while cur_len < max_new_tokens and paddle.any(unfinished_flag):
-                    input_ids, scores, unfinished_flag, model_kwargs = _post_process_(
-                        _forward_(**model_kwargs),
-                        input_ids,
-                        cur_len_gpu,
-                        origin_len_gpu,
-                        scores,
-                        unfinished_flag,
-                        model_kwargs,
-                        pad_token_id,
-                    )
-                    paddle.increment(cur_len)
-                    paddle.increment(cur_len_gpu)
-        else:
-            while cur_len < max_new_tokens and paddle.any(unfinished_flag):
-                input_ids, scores, unfinished_flag, model_kwargs = _post_process_(
-                    _forward_(**model_kwargs),
-                    input_ids,
-                    cur_len_gpu,
-                    origin_len_gpu,
-                    scores,
-                    unfinished_flag,
-                    model_kwargs,
-                    pad_token_id,
-                )
-                paddle.increment(cur_len)
-                paddle.increment(cur_len_gpu)
+        while cur_len < max_new_tokens and paddle.any(unfinished_flag):
+            input_ids, scores, unfinished_flag, model_kwargs = _post_process_(
+                _forward_(**model_kwargs),
+                input_ids,
+                cur_len_gpu,
+                origin_len_gpu,
+                scores,
+                unfinished_flag,
+                model_kwargs,
+                pad_token_id,
+            )
+            cur_len += 1
+            cur_len_gpu += 1
 
         return input_ids[:, origin_len:], scores
 

paddlenlp/server/predictor.py

@@ -21,6 +21,7 @@
 
 import paddle
 
+from ..utils.env import PADDLE_INFERENCE_MODEL_SUFFIX, PADDLE_INFERENCE_WEIGHTS_SUFFIX
 from ..utils.log import logger
 
 
@@ -40,13 +41,15 @@
 
     def _get_default_static_model_path(self):
         # The model path had the static_model_path
-        static_model_path = os.path.join(self._model_path, self._default_static_model_path, "inference.pdmodel")
+        static_model_path = os.path.join(
+            self._model_path, self._default_static_model_path, f"inference{PADDLE_INFERENCE_MODEL_SUFFIX}"
+        )
         if os.path.exists(static_model_path):
             return os.path.join(self._model_path, self._default_static_model_path, "inference")
         for file_name in os.listdir(self._model_path):
             # FIXME(wawltor) The path maybe not correct
-            if file_name.count(".pdmodel"):
-                return os.path.join(self._model_path, file_name[:-8])
+            if file_name.count(PADDLE_INFERENCE_MODEL_SUFFIX):
+                return os.path.join(self._model_path, file_name[: -len(PADDLE_INFERENCE_MODEL_SUFFIX)])
         return None
 
     def _is_int8_model(self, model_path):
@@ -110,7 +113,10 @@
         """
         Construct the input data and predictor in the PaddlePaddele static mode.
         """
-        self._config = paddle.inference.Config(static_model_path + ".pdmodel", static_model_path + ".pdiparams")
+        self._config = paddle.inference.Config(
+            static_model_path + PADDLE_INFERENCE_MODEL_SUFFIX,
+            static_model_path + PADDLE_INFERENCE_WEIGHTS_SUFFIX,
+        )
         self._config.disable_glog_info()
         if paddle.get_device() == "cpu":
             self._config.disable_gpu()
@@ -146,7 +152,7 @@
             os.mkdir(onnx_dir)
         float_onnx_file = os.path.join(onnx_dir, "model.onnx")
         if not os.path.exists(float_onnx_file):
-            model_path = static_model_path + ".pdmodel"
+            model_path = static_model_path + PADDLE_INFERENCE_MODEL_SUFFIX
             params_file = static_model_path + ".pdiparams"
             onnx_model = paddle2onnx.command.c_paddle_to_onnx(
                 model_file=model_path, params_file=params_file, opset_version=13, enable_onnx_checker=True

paddlenlp/taskflow/multimodal_feature_extraction.py

@@ -19,6 +19,7 @@
 from PIL import Image
 
 from ..transformers import AutoModel, AutoProcessor
+from ..utils.env import PADDLE_INFERENCE_MODEL_SUFFIX, PADDLE_INFERENCE_WEIGHTS_SUFFIX
 from ..utils.log import logger
 from .task import Task
 from .utils import dygraph_mode_guard, static_mode_guard
@@ -411,9 +412,9 @@
         self.inference_image_model_path = os.path.join(_base_path, "static", "get_image_features")
         self.inference_text_model_path = os.path.join(_base_path, "static", "get_text_features")
         if (
-            not os.path.exists(self.inference_image_model_path + ".pdiparams")
+            not os.path.exists(self.inference_image_model_path + PADDLE_INFERENCE_WEIGHTS_SUFFIX)
             or self._param_updated
-            or not os.path.exists(self.inference_text_model_path + ".pdiparams")
+            or not os.path.exists(self.inference_text_model_path + PADDLE_INFERENCE_WEIGHTS_SUFFIX)
         ):
             with dygraph_mode_guard():
                 self._construct_model(self.model)
@@ -422,8 +423,8 @@
         if self._predictor_type == "paddle-inference":
             # Get text inference model
             self.inference_model_path = self.inference_text_model_path
-            self._static_model_file = self.inference_model_path + ".pdmodel"
-            self._static_params_file = self.inference_model_path + ".pdiparams"
+            self._static_model_file = self.inference_model_path + PADDLE_INFERENCE_MODEL_SUFFIX
+            self._static_params_file = self.inference_model_path + PADDLE_INFERENCE_WEIGHTS_SUFFIX
             self._config = paddle.inference.Config(self._static_model_file, self._static_params_file)
             self._prepare_static_mode()
 
@@ -435,8 +436,8 @@
 
             # Get image inference model
             self.inference_model_path = self.inference_image_model_path
-            self._static_model_file = self.inference_model_path + ".pdmodel"
-            self._static_params_file = self.inference_model_path + ".pdiparams"
+            self._static_model_file = self.inference_model_path + PADDLE_INFERENCE_MODEL_SUFFIX
+            self._static_params_file = self.inference_model_path + PADDLE_INFERENCE_WEIGHTS_SUFFIX
             self._config = paddle.inference.Config(self._static_model_file, self._static_params_file)
             self._prepare_static_mode()
 
@@ -449,15 +450,15 @@
             # Get text onnx model
             self.export_type = "text"
             self.inference_model_path = self.inference_text_model_path
-            self._static_model_file = self.inference_model_path + ".pdmodel"
-            self._static_params_file = self.inference_model_path + ".pdiparams"
+            self._static_model_file = self.inference_model_path + PADDLE_INFERENCE_MODEL_SUFFIX
+            self._static_params_file = self.inference_model_path + PADDLE_INFERENCE_WEIGHTS_SUFFIX
             self._prepare_onnx_mode()
             self.predictor_map["text"] = self.predictor
 
             # Get image onnx model
             self.export_type = "image"
             self.inference_model_path = self.inference_image_model_path
-            self._static_model_file = self.inference_model_path + ".pdmodel"
-            self._static_params_file = self.inference_model_path + ".pdiparams"
+            self._static_model_file = self.inference_model_path + PADDLE_INFERENCE_MODEL_SUFFIX
+            self._static_params_file = self.inference_model_path + PADDLE_INFERENCE_WEIGHTS_SUFFIX
             self._prepare_onnx_mode()
             self.predictor_map["image"] = self.predictor